Big Data and Data Warehouse Architecture

Further development and new additions to the Hadoop framework, such as Stinger from HortonWorks or Impala from Cloudera try to bridge the gap between traditional EDWH architectures and big data architectures.
Especially Stinger.next initiative with the goal of speeding up Hive and delivering SQL 2011 standard to use on Map / Reduce Hadoop clusters makes this technology usable for developers with a SQL background. This next iteration in Hive optimization also brings an ACID framework with transactions and writeable tables. This is especially useful in data warehouse contexts, for example when you need to add meta data.

With these developments it seems plausible, that Hadoop and with it Big Data as a whole will move from ETL plattform for traditional EDWH architectures using traditional database systems, to a unified plattform, where Hadoop stores all data from raw unstructured data to structured data from the companies transactional systems and the meta data created in for reporting purposes. So access to all data would be given in the same system and query-able with SQL.
Standard reporting and deeper analysis on all data could then be accessed on the same system, so that all analysts and traditional BI developers share one platform and a better understanding of all the data needed and used in the data warehouse system.

I already did a benchmark on query speed for MySQL, Stinger and Impala here and will update this, once Stinger.next is out.

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